{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<link href='https://fonts.googleapis.com/css?family=Passion+One' rel='stylesheet' type='text/css'><style>div.attn { font-family: 'Helvetica Neue'; font-size: 30px; line-height: 40px; color: #FFFFFF; text-align: center; margin: 30px 0; border-width: 10px 0; border-style: solid; border-color: #5AAAAA; padding: 30px 0; background-color: #DDDDFF; }hr { border: 0; background-color: #ffffff; border-top: 1px solid black; }hr.major { border-top: 10px solid #5AAA5A; }hr.minor { border: none; background-color: #ffffff; border-top: 5px dotted #CC3333; }div.bubble { width: 65%; padding: 20px; background: #DDDDDD; border-radius: 15px; margin: 0 auto; font-style: italic; color: #f00; }em { color: #AAA; }div.c1{visibility:hidden;margin:0;height:0;}div.note{color:red;}</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Ebnable HTML/CSS \n",
    "from IPython.core.display import HTML\n",
    "HTML(\"<link href='https://fonts.googleapis.com/css?family=Passion+One' rel='stylesheet' type='text/css'><style>div.attn { font-family: 'Helvetica Neue'; font-size: 30px; line-height: 40px; color: #FFFFFF; text-align: center; margin: 30px 0; border-width: 10px 0; border-style: solid; border-color: #5AAAAA; padding: 30px 0; background-color: #DDDDFF; }hr { border: 0; background-color: #ffffff; border-top: 1px solid black; }hr.major { border-top: 10px solid #5AAA5A; }hr.minor { border: none; background-color: #ffffff; border-top: 5px dotted #CC3333; }div.bubble { width: 65%; padding: 20px; background: #DDDDDD; border-radius: 15px; margin: 0 auto; font-style: italic; color: #f00; }em { color: #AAA; }div.c1{visibility:hidden;margin:0;height:0;}div.note{color:red;}</style>\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "Enter Team Member Names here (double click to edit):\n",
    "\n",
    "- Name 1:\n",
    "- Name 2:\n",
    "- Name 3:\n",
    "\n",
    "________\n",
    "\n",
    "# In Class Assignment Four\n",
    "In the following assignment you will be asked to fill in python code and derivations for a number of different problems. Please read all instructions carefully and turn in the rendered notebook (or HTML of the rendered notebook) before the end of class. Be sure to save the notebook before uploading!\n",
    "\n",
    "<a id=\"top\"></a>\n",
    "## Contents\n",
    "* <a href=\"#cluster\">Create Clustering Data</a>\n",
    "* <a href=\"#kmeans\">K-means Clustering</a>\n",
    "\n",
    "** Available during live session: **\n",
    "* <a href=\"#mini\">MiniBatch K-Means</a>\n",
    "* <a href=\"#dbscan\">Using DBSCAN</a>\n",
    "\n",
    "________________________________________________________________________________________________________\n",
    "<a id=\"cluster\"></a>\n",
    "<a href=\"#top\">Back to Top</a>\n",
    "## Clustering\n",
    "Please run the following code to create synthetic datasets on a two dimensional plane. Three sets of data are created and saved into variables `X1`, `X2`, and `X3`. Each dataset is plotted afterward. We will be using `scikit-learn` to perform clustering on each dataset. You do not need to understand the specifics of the code in the next block, just know that three datasets are created with two attributes in each dataset (*i.e.*, two columns), and they are saved into variables `X1`, `X2`, and `X3`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/eclarson/anaconda/envs/MLEnv/lib/python3.5/site-packages/ipykernel/__main__.py:38: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "/Users/eclarson/anaconda/envs/MLEnv/lib/python3.5/site-packages/ipykernel/__main__.py:39: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
    },
    {
     "data": {
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AhGlnQi2LlMsPyuUH5fIjRLliszOefRobDyB/sOcLQJh2xocQy5ov1CEMYtchdvmB+HWo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\n3nWnkWq36k4TvY5dv75YNp8AA5drzP6h7njUF7ZXzg9910n9WVmL+ef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TAAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1046b0e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# getting things started\n",
    "%matplotlib inline\n",
    "\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from __future__ import print_function\n",
    "\n",
    "# generate some sample data\n",
    "n_samples = 1700\n",
    "np.random.seed(0)\n",
    "\n",
    "#======= Sample data ONE\n",
    "cluster_centers = [[1.0,1], [1,2], [2,1], [2,2], [3,1], [3,2], [4,1], [4,2], [5,1], [5,2]]\n",
    "X1 = np.array(cluster_centers) # cluster prototypes\n",
    "X1 = np.repeat(X1, 150, axis=0)\n",
    "X1 += .2 * np.random.randn(X1.shape[0],2) # add some randomness\n",
    "\n",
    "#====== Sample data TWO\n",
    "# Generate some harder sample data\n",
    "t = 1.5 * np.pi * (1 + 3 * np.random.rand(1, n_samples))\n",
    "x = t * np.cos(t) / 10.0\n",
    "y = t * np.sin(t) / 10.0\n",
    "\n",
    "X2 = np.concatenate((x, y))\n",
    "X2 += .1 * np.random.randn(2, n_samples) # add some randomness\n",
    "X2 = X2.T # and transpose it\n",
    "\n",
    "#====== Sample data THREE\n",
    "cluster_centers[0] = [0.75,0.75]\n",
    "cluster_centers.insert(0,[0.75,0.75])\n",
    "X3 = np.array(cluster_centers) # cluster prototypes\n",
    "X3 = np.repeat(X3, 150, axis=0)\n",
    "X3 += .1 * np.random.randn(X3.shape[0],2) # add some randomness\n",
    "X3[0:300] += .3 * np.random.randn(300,2) # add spread to first class\n",
    "\n",
    "n_samples = len(X3)/4*3\n",
    "Xtmp1 = X3[0:n_samples] + .4 * np.random.randn(n_samples,2) # create some spread in the points\n",
    "Xtmp2 = X3[0:n_samples] + .4 * np.random.randn(n_samples,2) # create some spread in the points\n",
    "X3 = np.concatenate((X3,Xtmp1,Xtmp2)) # and add them back in for differing density\n",
    "\n",
    "# now plot each dataset\n",
    "plt.figure(figsize=(15,5))\n",
    "for i,X in enumerate([X1,X2,X3]):\n",
    "    plt.subplot(1,3,i+1)\n",
    "    plt.plot(X[:, 0], X[:, 1], 'r.', markersize=2) #plot the data\n",
    "    plt.title('Dataset name: X{0}'.format(i+1))\n",
    "    plt.xlabel('X{0}, Feature 1'.format(i+1))\n",
    "    plt.ylabel('X{0}, Feature 2'.format(i+1))\n",
    "    plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Questions**: For each dataset, is it: \n",
    "- best described as center-based, contiguous, or density based (or a mix of more than one)? \n",
    "- best described as partitional or hierarchical?  \n",
    "\n",
    "**Question**: Given the plots above for each dataset, what type of clustering algorithm would you consider using for each and why? That is, give your opinion on whether k-means, hierarchical agglomerative, and/or DBSCAN are appropriate for each dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "Enter your answer here:\n",
    "\n",
    "*Double Click to Edit*\n",
    "\n",
    "\n",
    "\n",
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "<a id=\"kmeans\"></a>\n",
    "<a href=\"#top\">Back to Top</a>\n",
    "## Kmeans Clustering in Scikit\n",
    "Now lets look at calculations in K-Means clustering with `scikit-learn` and see if we can calculate the difference between two different clusterings. Lets start by using k-means clustering on the first dataset. The code is given for you below as well as code for plotting the centroids, as shown. Take a look to see how k-means is run.\n",
    "\n",
    "You can also see the documentation for k-means here:\n",
    "http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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B7WYObZYM2s0c2qxYKDRIKKeeemrZSagltJs5tFkyaDdzaLNiYYwGIYQQQkJhjAYhhBBC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\nJ7GfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e165e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# run kmeans algorithm (this is the most traditional use of k-means)\n",
    "kmeans = KMeans(init='random', # initialization\n",
    "        n_clusters=10,  # number of clusters\n",
    "        n_init=1,       # number of different times to run k-means\n",
    "        n_jobs=-1)\n",
    "\n",
    "kmeans.fit(X1)\n",
    "\n",
    "# visualize the data\n",
    "centroids = kmeans.cluster_centers_\n",
    "plt.plot(X1[:, 0], X1[:, 1], 'r.', markersize=2) #plot the data\n",
    "plt.scatter(centroids[:, 0], centroids[:, 1],\n",
    "            marker='+', s=200, linewidths=3, color='k')  # plot the centroids\n",
    "plt.title('K-means clustering for X1')\n",
    "plt.xlabel('X1, Feature 1')\n",
    "plt.ylabel('X1, Feature 2')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Questions**: \n",
    "- Is the above a good clustering of the data? Why or why not? \n",
    "- Run the block of code a few times. Did the results ever improve? Is the clustering consistent? \n",
    "- How might we make the clustering more consistent in finding the optimal clustering (i.e., the clustering with the smallest SSE)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "Enter your answer here:\n",
    "\n",
    "*Double Click to Edit*\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## K-means consistency\n",
    "**Exercise**: Now let's try to make the kmeans clustering more consistent. Change the `n_init` and `init` parameters of kmeans such that (almost) every time you run the block of code, the optimal clustering is found. Make sure that the solution you find is as *efficient as possible*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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srDaikwD+BsB/OPhgb4XGuHuFTUd28Srkgc17NhCmYUJj8eLF+Q3XmJAmn2bd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\nzMzMSuNEw8zMzErjRMPMzMxK8zc2TbRtHMK8PgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105420ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "#--------Change parameters below this line---------------------------\n",
    "# CHANGE THE PARAMETERS HERE TO GET A CONSISTENTLY OPTIMAL CLUSTERING\n",
    "# run kmeans algorithm (this is the most traditional use of k-means)\n",
    "kmeans = KMeans(\n",
    "    init='random',    # initialization\n",
    "    n_clusters=10,    # number of clusters\n",
    "    n_init=1,         # number of different times to run k-means\n",
    "    n_jobs=-1)\n",
    "#--------Change parameters above this line---------------------------\n",
    "\n",
    "kmeans.fit(X1)\n",
    "\n",
    "# visualize the data\n",
    "centroids = kmeans.cluster_centers_\n",
    "plt.plot(X1[:, 0], X1[:, 1], 'r.', markersize=2) #plot the data\n",
    "plt.scatter(centroids[:, 0], centroids[:, 1],\n",
    "            marker='+', s=200, linewidths=3, color='k')  # plot the centroids\n",
    "plt.title('K-means clustering for X1')\n",
    "plt.xlabel('X1, Feature 1')\n",
    "plt.ylabel('X1, Feature 2')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": []
  }
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